Optimized method for lid biosensor resonance detection

ABSTRACT

An optical interrogation system is described herein that can interrogate a label-independent-detection (LID) biosensor and monitor a biological event on top of the biosensor without suffering from problematical parasitic reflections and/or problematical pixelation effects. In one embodiment, the optical interrogation system is capable of interrogating a biosensor and using a low pass filter algorithm to digitally remove problematic parasitic reflections contained in the spectrum of an optical resonance which makes it easier to determine whether or not a biological event occurred on the biosensor. In another embodiment, the optical interrogation system is capable of interrogating a biosensor and using an oversampling/smoothing algorithm to reduce oscillations in the estimated location of an optical resonance caused by the problematical pixelation effect which makes it easier to determine whether or not a biological event occurred on the biosensor.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 11/716,425, filed Mar. 9, 2007, now pending, whichclaims the benefit of U.S. Provisional Patent Application Ser. No.60/781,397 filed Mar. 10, 2006. The contents of these documents arehereby incorporated by reference herein.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an optical interrogation system thatcan interrogate a label-independent-detection (LID) biosensor andmonitor a biological event on top of the biosensor without sufferingfrom problematical parasitic reflections and/or problematical pixelationeffects.

2. Description of Related Art

Today non-contact optical sensor technology is used in many areas ofbiological research to help perform increasingly sensitive andtime-constrained assays. In these assays, an optical interrogationsystem is used to monitor changes in the refractive index or variationsin the optical response/optical resonance of an optical biosensor as abiological substance is brought into a sensing region of the biosensor.The presence of the biological substance alters the optical resonance ofthe biosensor when it causes a bio-chemical interaction like materialbinding, adsorption etc . . . It is this alteration of the opticalresonance that enables one to use the biosensor to directly monitorbiological events in label-free assays where the expense andexperimental perturbations of fluorescent dyes are completely avoided.

The optical interrogation system needs to implement some sort ofresonance detection algorithm to determine whether or not a biologicalevent (e.g., binding of a drug to a protein) occurred on the biosensor.To ensure that one can detect a small biochemical binding event, theresonance detection algorithm needs to be designed to sense small shiftsin the resonance spectral location or the resonance angular location,wherein the shifts are often a very small fraction of the resonancewidth itself. For example, a typical resonance width for a resonantwaveguide grating biosensor may be ˜1 nm, but a small biochemicalbinding event might present a change of only ˜0.001 nm. Unfortunately,today it is difficult to properly optimize the resonance detectionalgorithm because both the resolution and linearity of the opticalresonance of a biosensor 102 may be adversely affected by: (1) thepresence of measurement noise caused by problematical parasiticreflections; and/or (2) the presence of measurement artifacts caused byproblematical pixelation effects. Thus, there is a need for an opticalinterrogation system that can optimize the detection of the opticalresonance by addressing the problematical parasitic reflections and/orproblematical pixelation effects. This need and other needs aresatisfied by the optical interrogation system and method of the presentinvention.

BRIEF DESCRIPTION OF THE INVENTION

The present invention includes an optical interrogation system that caninterrogate a label-independent-detection (LID) biosensor and monitor abiological event on top of the biosensor without suffering fromproblematical parasitic reflections and/or problematical pixelationeffects. In one embodiment, the optical interrogation system is capableof interrogating a biosensor and using a low pass filter algorithm todigitally remove problematic parasitic reflections contained in thespectrum of an optical resonance which makes it easier to determinewhether or not a biological event occurred on the biosensor. In anotherembodiment, the optical interrogation system is capable of interrogatinga biosensor and using an oversampling/smoothing algorithm to reduceoscillations in the estimated location of an optical resonance caused bythe problematical pixelation effect which makes it easier to determinewhether or not a biological event occurred on the biosensor.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention may be had byreference to the following detailed description when taken inconjunction with the accompanying drawings wherein:

FIG. 1 is a block diagram of an optical interrogation system configuredto function in accordance with two different embodiments of the presentinvention;

FIGS. 2-10 are drawings and graphs used to help describe how the opticalinterrogation system can function to reduce measurement noise caused byproblematical parasitic reflections in accordance with the firstembodiment of the present invention; and

FIGS. 11-24 are drawings and graphs used to help describe how theoptical interrogation system can function to reduce measurementartifacts caused by problematical pixelation effects in accordance withthe second embodiment of the present invention.

DETAILED DESCRIPTION OF THE DRAWINGS

Referring to FIG. 1, there is a block diagram of an opticalinterrogation system 100 that can interrogate a biosensor 102 inaccordance with the present invention. As shown, the opticalinterrogation system 100 has a launch system 101 which includes a lightsource 104 (e.g., broad spectrum light source 104) that outputs anoptical beam 106 (e.g., white light beam 106) into a lensed fiber optic108 which emits the optical beam 106 towards the biosensor 102 (e.g.,grating coupled waveguide biosensor 102). The optical interrogationsystem also includes a receive system 103 which has a lensed fiber optic112 that receives an optical beam 110 reflected from the biosensor 102.Alternatively, the launch optic 108 and receive optic 112 can be asingle optic, an exemplary single fiber interrogation system isdisclosed in co-assigned U.S. patent application Ser. No. 11/058,155filed on Feb. 14, 2005. The contents of this document are incorporatedby reference herein. The receive system 103 also includes a detector 114(e.g., spectrometer 114, CCD array 140) which receives the reflectedoptical beam 110 from the lensed fiber optic 112. The detector 114outputs a signal 116 (which is representative of the spectral resonance117) to a processor 118. The processor 118 processes the signal 116 andoptimizes the detection of the position of the spectral resonance 117 byaddressing the problematical parasitic reflections and/or theproblematical pixelation effects. Then, the processor 118 outputs anoptimized signal 120 which is used to monitor a biological event (e.g.,biological binding of ligand to analyte) on top of the biosensor 102.How the processor 118 optimizes the signal 116 is described in detailafter a brief description is provided about the structure and operationof the biosensor 102.

The biosensor 102 makes use of changes in the refractive index at itstop surface that affect the waveguide coupling properties of the emittedoptical beam 106 and the reflected optical beam 110. These changesenable the label-free monitoring of a biological event such as whetheror not a biological substance 122 (e.g., cell, molecule, protein, drug,chemical compound, nucleic acid, peptide, carbohydrate) happens to belocated on the biosensor's superstrate 124 (sensing region 124). Forinstance, the biological substance 122 is typically located within abulk fluid which is deposited on the biosensor's superstrate 124. And,it is the presence of this biological substance 122 in the bulk fluidthat alters the index of refraction at the biosensor's top surface 126.

The biosensor's 102 functionality may be best understood by analyzingthe structure of its diffraction grating 128 and waveguide 130. Theoptical beam 106 that is directed at the diffraction grating 128 canonly be coupled into the waveguide 130 if its wave vector satisfies thefollowing resonant condition as shown in equation no. 1:

k _(x) ′=k _(x)−κ  [1]

where k_(x)′ is the x-component of the incident wave vector, K_(x) isthe guided mode wave vector, and κ is the grating vector. The gratingvector κ is defined as a vector having a direction perpendicular to thelines of the diffraction grating 128 and a magnitude given by 2π/Λ whereΛ is the grating period (pitch). This expression may also be written interms of wavelength λ and incident angle θ as shown in equation no. 2:

$\begin{matrix}{{\frac{2\pi \; n_{inc}}{\lambda}\sin \; \theta} = {\frac{2\pi \; n_{eff}}{\lambda} - \frac{2\; \pi}{\Lambda}}} & \lbrack 2\rbrack\end{matrix}$

where θ is the angle of incidence of the optical beam 106, N_(inc) isthe index of refraction of the incident medium, λ is the wavelength ofthe optical beam 106, and n_(eff) is the effective index of refractionof the waveguide 130. The waveguide 130 has an effective index ofrefraction that is a weighted average of the indices of refraction thatthe optical waveguide mode field “sees” as it propagates through thewaveguide 130. The optical waveguide mode preferably has a spatialextent that is much wider than the waveguide 130, where the spatialextent depends on the refractive index of the substrate 132. As aresult, the optical waveguide mode has an evanescent wave/tail thatextends into the superstrate 124 (sensing region 124) which “sees” anysurface changes created when the biological substance 122 approaches orcomes in contact with the biosensor's top surface 126.

The previous expression shown in equation no. 2 may be rewritten in themore convenient form shown in equation no. 3:

$\begin{matrix}{{\sin \; \theta} = {n_{eff} - \frac{\lambda}{\Lambda}}} & \lbrack 3\rbrack\end{matrix}$

which is the equation of a line where sin θ being the y axis, λ beingthe x-axis, Λn_(eff) the x-intercept, and −1/Λ the slope. To obtainequation no. 3, n_(inc) is set to 1 so that it could be removed fromthis expression. This approximation is used since air (n˜1.0003) is themost common incident medium. This relation is pictured in the graphshown in FIG. 2. When a biological substance 122 binds to the surface126, then the effective index of the waveguide 122 is altered whichleads to the shifting the resonant wavelength or resonant angle of thebiosensor 102. This shifting can be seen as a shift of the x-interceptin the line shown in FIG. 2.

The resonant condition (e.g., resonant wavelength or resonant angle) ofsuch a biosensor 102 may be interrogated to determine refractive indexchanges by observing the optical beam 110 reflected from the biosensor102. There are two different modes of operation for monitoringrefractive index changes from such a resonant waveguide gratingbiosensor 102—angular interrogation or spectral interrogation. Inangular interrogation, a nominally single wavelength optical beam 106 isfocused to create a range of illumination angles and directed into thebiosensor 102. The reflected optical beam 110 is received by thedetector 114 (e.g., CCD array 114). And, by monitoring the position ofthe resonant angle reflected by the biosensor 102, one can monitorbinding or refractive index changes on or near the biosensor's surface126. The angular interrogation concept is graphically represented in thegraph shown in FIG. 3. In spectral interrogation, a nominallycollimated, broadband optical beam 106 is sent into the biosensor 102and the reflected optical beam 110 is collected and sent to the detector114 (e.g., spectrometer 114). And, by observing the spectral location ofthe resonant wavelength (peak), one can monitor binding or refractiveindex changes on or near the biosensor's surface 126. The spectralinterrogation concept is graphically represented in the graph shown inFIG. 4. In the present invention, the focus in the description is on themethod of spectral interrogation even though the present invention canbe partly used for either interrogation method. In addition, the presentinvention can focus on an instrument configuration 100 where one sends awide spectrum to the biosensor 102 and measures the wavelength that isreflected by the biosensor 102. And, the same concepts of the presentinvention can also be used in an instrument configuration 100 that usesa tunable wavelength source 104 and measures the power reflected by thebiosensor 102 as a function of the wavelength of the tunable wavelengthsource 104.

Filtering Interference Fringes

One problem commonly associated with interrogating the biosensor 102 iscaused when a part of the optical beam 106 is reflected on the firstface of the biosensor's substrate 132 before the optical beam 106 isreflected by the biosensor's top surface 126. Once the optical beam 106is reflected by the biosensor's top surface 126, a part of it can alsobe reflected again by the first face of the biosensor's substrate 132.These parasitic reflections 106′ and 110′ are shown in FIG. 5. Thepresence of parasitic reflections 106′ and 110′ cause the generation offringes 134 in the received optical beam 106′, 110 and 110′ that areequivalent to Fabry-Perot cavity fringes. FIG. 6 is a graph thatillustrates the spectrum of a spectral resonance 117 which has thesefringes 134. This graph was generated by a high resolution spectrometer114 which had a resolution that was much smaller than the period of thefringes 134.

A known solution that can be used to reduce the problem associated withthe fringes 134 caused by the parasitic reflection 106′ includesinserting an optical isolator 136 between the lensed fibers 108 and 112and the biosensor's substrate 132 (see FIG. 1). This solution isdescribed in the co-assigned U.S. Patent Application No. US20050264818A1 entitled “Optical Interrogation Systems with Reduced ParasiticReflections and a Method for Filtering Parasitic Reflections”. Thecontents of this document are incorporated by reference herein.

The optical isolator 136 works well to filter out the parasiticreflection 106′ which is reflected from the first face of the substrate132. However, the optical isolator 136 can not filter the parasiticreflection 110′ created within the biosensor 102. Because, once theoptical beam 110 has been reflected within the biosensor 102, it becomeslinearly polarized. As a result, the optical isolator 136 can not filterthe parasitic reflection 110′. As shown in FIG. 7, the use of theoptical isolator 136 significantly attenuates the fringes 134 in thetails of the spectral resonance 117 but some residual modulation canstill be observed (compare to FIG. 6 in which an optical isolator 136was not used).

The visibility of the fringes 134 is a function of four main factors.The first factor is the spectrometer's resolution. The second factor isthe signal sampling which depends on the spectrometer's pixel size anddispersion. The third factor is the width of the spectral resonance 117.And, the fourth factor is the fringe period which depends on thethickness and index of refraction of the biosensor's substrate 132. Forexample, FIG. 8 is a graph that shows a spectral resonance 117 that wasexperimentally obtained by a medium resolution spectrometer 114 (onewhose resolution is comparable to the fringe period). In this example,the optical resonance's width was approximately 0.9 nm, thespectrometer's resolution was 0.18 nm and the sampling was 0.09 nm. Thefringe period was on the order of 0.33 nm.

The present invention removes/reduces the impact of these fringes 134 onthe resonance position determination by applying a low pass filter 138to the measured signal 116/spectral resonance 117. There are severaldifferent types of low pass filters 138 that can be used. For instance,one can calculate the convolution product between the measured signal116 and another function that can be a rectangular function or aGaussian function (for example). This exemplary low pass filter 138 isrepresented as follows:

Y′=G

y

-   -   Where    -   y is the signal    -   represents a convolution product    -   G is the filter function (e.g., Gaussian, rectangle (boxcar        function), sinc, . . . )    -   Y′ is the filtered signal 120

In another example, one can calculate the Fourier transform of themeasured signal 116 and then multiply this Fourier transform by a filterfunction. The filtered signal 120 is then obtained as the inverseFourier transform of this product. This exemplary low pass filter 138 isrepresented as follows:

Y1=G*FFT(y)

Y′=FFT ⁻¹(Y1)

-   -   Where    -   y is the signal    -   G is the filter function    -   FFT is the Fourier transform    -   FFT⁻¹ is the inverse Fourier transform    -   Y′ is the filtered signal 120

Another solution to this problem can also consist of intentionallydecreasing the resolution of the spectrometer 114. One way to do thiswith a conventional grating based spectrometer 114, involves misaligningthe focus of the entrance spectrometer slit or fiber. Although thissolution works, it is not the preferred approach because of the factthat measurement noise is proportional to the square root of theresonance width. Thus, when the spectrometer 114 is misaligned, thewidth of the spectral resonance 117 increases which in turn increasesthe uncertainty in the estimate of the location of the spectralresonance 117.

To validate the advantages of using the low pass filter algorithm 138, atypical spectral resonance 117 including parasitic reflection fringes134 was calculated. Then, the maximum deviation (or fringe errorcontribution) of the resonance location, estimated by a centroidalgorithm, from the true resonance location was calculated. The fringeerror contribution was generated in an estimate of the opticalresonance's location as fringes 114 were moved across the resonance peakby changing their phase (or location relative to the resonance peak).For example, a centroid calculation was performed to estimate thelocation of the optical resonance. FIG. 9 is a graph that shows theevolution of this fringe error contribution as a function of the widthof the low pass filter 138 that was applied.

Ideally, one should use a low pass filter 138 with a width which is wideenough to suppress the detrimental effects of the parasitic fringes 134.In theory, there is no upper limit to the width of the low pass filter138. Indeed, by increasing the low pass filter's width, the resonance isbroadened and the measurement noise is filtered. So, although theresonance gets wider because of the low pass filter 138, the noise onthe resonance position is not affected. In practice, FIG. 9 shows thatthere is a limit on the width of the low pass filter 138 which occurswhen the fringe error contribution does not change anymore afterincreasing the width. In the example shown in FIG. 9, the bestcompromise entails using a low pass filter 138 with a width of around500 pm.

To implement this embodiment of the present invention, it should benoted that the biosensor 102 combined with the optical interrogationsystem 100 should satisfy two conditions:

-   -   1. The width of the spectral resonance 117 should be        significantly larger than the period of the fringes 134. This is        equivalent to stating that, when calculating the Fourier        transform of the signal, the central lobe associated with the        useful part of the signal must be well separated from any side        lobes associated with the fringes of higher frequency        modulation.    -   2. The reflected signal 110 should be sampled at a period        significantly lower than the period of the fringes 134. If this        condition is not fulfilled, then the fringes 134 will generate        apparent low frequency deformations of the spectral resonance        117. This undesirable effect is also known as aliasing.

FIG. 10 is a flowchart illustrating the steps of a method 1000 for usingthe optical interrogation system 100 to interrogate a biosensor 102 andat the same time reduce the measurement noise caused by problematicalparasitic reflections 134 in accordance with the first embodiment of thepresent invention. Beginning at step 1002, the optical interrogationsystem 100 and in particular a launch system 101 emits an optical beam106 towards the biosensor 102. At step 1004, the optical interrogationsystem 100 and in particular a receive system 103 collects an opticalbeam 110 from the biosensor 102. At step 1006, the optical interrogationsystem 100 and in particular a spectrometer 114 generates a signal 116which corresponds to the collected optical 110. Then at step 1008, theoptical interrogation system 100 and in particular a processor 118applies a low pass filter 138 to the signal 116 to digitallyremove/reduce the problematic parasitic reflections 134 that are locatedon each side of a spectral resonance 117.

Spectrometer Pixelation

A second problem that the present invention addresses is caused by thefinite size of the CCD pixels in the spectrometer 114. As shown in FIG.1, light 110 enters the spectrometer 114 and is dispersed and sent to aCCD (charge-coupled device) array 140. Each pixel in the CCD array 140is mapped to a specific spectral region. This enables the processor 118to obtain and record a spectrum that shows light intensity as a functionof wavelength (or a function of the pixel in the spectrometer 114) whenthe CCD array 140 is read-out. The optical information that is recordedis “pixelated” that is, it is sampled in finite bins each with a widthequal to the width of the respective CCD pixels in the spectrometer 114.

Because each CCD pixel effectively integrates all of spectral energythat falls within it, this can distort the apparent shape of thespectral resonance 117. An example of such a distortion from such apixelated, or sampled, spectrum is shown in FIG. 11. The amount ofdistortion depends on the locations of the edges of the CCD pixelsrelative to the peak of the spectral resonance 117. As such, when thespectral resonance 117 moves across the CCD pixels, this causes aperiodic error function in the estimate of location of the spectralresonance 117. This periodic error function or “pixelation oscillation”has a period equal to the size of the CCD pixel. And, the amplitude ofthe periodic error function depends on the optical resonance width, theCCD pixel size, and the particular algorithm that is chosen to calculatethe location of the spectral resonance 117. This pixelation problem isnot unique to the spectral method of detection, indeed, it can occur forany sampled spectrum. As such, this problem appears as well in anangular interrogation system that uses a CCD array to spatially samplethe reflected angular intensity.

To calculate a periodic error caused by the limited CCD pixel size, atheoretical spectral resonance 117 was simulated and integrated overeach CCD pixel. Then, a centroid of the spectral resonance 117 wascalculated which has a threshold by applying the following algorithm forall of the points that are above the threshold value:

-   -   For all Y>threshold:

Y′=Y−threshold

Centroid=(ΣX*Y′)/(ΣY′)

where Y is the response of a given CCD pixel, and X is the wavelengthmeasured at a given CCD pixel. The purpose of the threshold is toexclude background that is not part of the spectral resonance 117. Thisconcept of a threshold for the algorithm is graphically illustrated inFIG. 12.

The centroid that is calculated by this algorithm is a function of thelocation of the spectral resonance 117 on the CCD array. FIG. 13 is agraph that shows a periodic error function which is the deviationbetween the actual location of the spectral resonance 117 and thealgorithm's estimate of the optical resonance's location. This periodicerror function was obtained by assuming a 0.85 nm resonance width and a0.09 nm pixel size. In addition, the spectral resonance 117 was assumedto move 0.27 nm and the threshold was set at 25% of the maximum power inthe optical resonance's peak.

One approach that can be used to minimize the pixelation error involvesdecreasing (lowering) the threshold level. FIG. 14 is a graph that showsa pixelation error that was calculated as a function of the thresholdlevel. As can be seen, the further the threshold is lowered, the greaterthe amount of signal energy that will be included in the calculation ofthe resonance location, and the more accurate the location estimatebecomes. In contrast, the higher the threshold means that less signalenergy will be included in the calculation of the resonance location,and the less accurate the location estimate becomes.

However, a problem with decreasing the threshold level is that thewavelength window over which the centroid is calculated is madeconsiderable larger. This means that the centroid calculation includesnot just more energy from the spectral resonance 117, but it alsoincludes energy from noise sources (e.g., detector dark current) whichare always present in a practical optical interrogation system.Moreover, as the threshold line is dropped further and further, onlysmall incremental amounts of additional signal energy are added, sincethe resonance amplitude drops off, but a lot of noise energy is added,since the noise typically scales with the bandwidth used in thecalculation (i.e. the number of CCD pixels or wavelength rangeincluded). As such, the dropping of the threshold line all the way tozero to avoid pixelation induced error would considerably increase thenoise content and impair the estimate of the optical resonance location.

To help illustrate the impact of the threshold on the measurement noise,FIG. 15 is provided which shows a theoretical model of the impact oflowering the threshold on the measurement noise. And, the impact ofraising the threshold level in an experiment with a centroid calculationis shown in the graphs of FIG. 16. In this experiment, a 10 minutemeasurement of 384 resonant waveguide grating sensors 102 placed in amicro well plate, and soaked with water, was used to evaluate thesystem's noise. It can be clearly seen that the raising of the thresholdenhances the system's performance. However, as the threshold isincreased, the pixelation induced error becomes a problem.

Alternative algorithms besides the centroid algorithm have been used inthe past to help remove this pixelation induced error. Examples of thesealternative algorithms include, but are not limited to: (1) peak fittingto a known resonance shape; (2) using a knife edge function; and (3)using a correlation function with a known resonance shape. Some of thesealgorithms seek to remove the pixelation of the data by essentially“undoing” the integration of the signal energy that was performed by theCCD pixels. However, all of these algorithms still suffer from the sameproblem that is associated with the centroid algorithm. Again, if analgorithm does not use any threshold, then the tails 134 of the spectralresonance 117 are involved in the calculation and the measurement noiseincreases. And, if an algorithm uses a threshold, then the same periodicerror function is observed as was in the centroid algorithm.

The present invention addresses this problem by numerically“oversampling” the spectral resonance 117 and then calculating acentroid over the “oversampled” signal. The oversampling entailsreplicating each data point into N data points with a signal orintensity value identical to the original point, but positioned inwavelength (or pixel number) at steps of Δλ/N, rather than the originaldata spacing of Δλ. In this way, the data array is expanded by a factorN. A graphic illustration of such an oversampling process 142 is shownin FIG. 17.

Once, the data is oversampled then a low pass filter (like the onedescribed in the first embodiment), interpolation, or some othersmoothing operation is applied. The centroid is then calculated on thesmoothed signal. Alternatively, the smoothing operation can be performedin the oversampling process. And, now when the spectral resonance 117moves across the CCD pixels the errors caused by the pixelation inducedoscillation are reduced or eliminated. Also of note is that thiselimination of the pixelation induced oscillation can be accomplished ifthe oversampling and filtering are performed on the entire spectralresonance 117 or if they are performed only close to the area were thethreshold in the centroid crosses the resonance curve (see FIG. 12).

It should be appreciated that this technique is not the only techniquethat may be used to reduce the impact of pixelation. Some exemplaryalgorithms that can be used for signal oversampling (and possiblefiltering) in accordance with the present invention are as follows:

Exemplary Algorithm 1: Oversampling using a cubic spline interpolation:

-   -   The cubic spline effects both an oversampling and a smoothing        since an interpolation is implicit in the cubic spline        technique. Therefore, no additional low pass filter is        necessary.

Exemplary Algorithm 2: Fourier method:

-   -   Calculate the Fourier transform of the spectral resonance 117.    -   Multiply by a low pass filter function to filter the fringes 134        and eliminate the power at high frequencies.    -   Add zero's on the left and right of the filtered function.    -   Calculate the inverse Fourier transform to obtain the        filtered/oversampled optical resonance 120.

Exemplary Algorithm 3: Step function:

-   -   Oversample the spectral resonance 117 to make it look like a        stairstep function.    -   Convolve the oversampled optical resonance by a filter function        such as a Gaussian function. This simultaneously filters the        fringes 134 and smoothes the steps of the oversampled optical        resonance.

FIG. 18 is a graph that shows a calculation of the periodic errorfunction that was made with and without oversampling. The function 1802that shows a variation of approximately 2.5 pm was calculated with onlya centroid algorithm (see also FIG. 13). And, the two other functions1804 (# oversamples per pixel=5) and 1806 (# oversamples per pixel=6)where calculated with the oversampling process 142 in accordance withthe present invention.

To validate the oversampling process, an experiment was conducted with aLID microplate. In the experiment, hot water was placed into one of thesensor wells to generate a spectral resonance 117 whose spectralposition varied over time. Then, by using a fiber optic beam splitter,the reflected light was split and sent into 8 different spectrometers(this setup is not shown). If the detected spectral resonance 117 isfree of measurement errors such as fringes 134 and pixelation, then the8 centroids should perfectly track each other within any constant offsetof each spectrometer.

FIG. 19 is a graph that shows the evolution of the centroid observed bythe 8 spectrometers that was calculated when the centroid algorithm wasdirectly applied without filtering or oversampling. In this case, aconstant offset (calibration) was removed from each spectrometer, byassigning the first measurement of each spectrometer to a value of zerowavelength shift. As a result, the subsequent measurements were allmeasured as a shift relative to the starting wavelength. As can be seen,there is an initial downward spike when the hot water was added, andthen a slow exponential rise in wavelength as the water above the sensorcooled. It can also be seen that all 8 spectrometers observed a verysimilar wavelength shift. Indeed, based on the scale shown in FIG. 19 itcan be seen that all 8 traces are “almost” completely overlaid on top ofone another.

At first glance this appears to be good, but when an optical detectionsystem 100 interrogates a RWG sensor 102 (resonance waveguide gratingsensor 102) then even a very small wavelength measurement discrepancycan be significant to the end user. For instance, if a small biochemicalbinding signal may ride upon a large change in a bulk refractive indexchange. Then, to reference out the bulk refractive index change, onespectrometer may be used observe a sensor 102 in a well with thebiochemical binding plus the bulk refractive index shift. And, anotherspectrometer may be used to observe a sensor 102 in a control well withno biochemical binding but with the same bulk refraction index solutionadded. If both spectrometers report the exact same bulk refractive indexchange, then the bulk index shift may be referenced out by subtractingthe two signals, leaving only the biochemical biding shift of interest.Unfortunately, the bulk refractive index change could be as large as 100pm (10⁻³ RIU), while the binding signal could be as small as 1 pm (10⁻⁵RIU). In that case, both spectrometers must report the same bulkrefractive index change to an accuracy of <<1 pm (<<1% of the bulk indexshift), otherwise the small 1 pm binding signal will be overwhelmed bymeasurement uncertainty. Therefore, ensuring the linearity of thespectrometer or algorithm response to levels of ˜100 fm is important.

To visualize the peak location errors of each spectrometer on such afine scale, one can calculate the difference (or residual) between thepeak location calculated by each spectrometer and the mean value of thepeak location calculated by all 8 spectrometers. FIG. 20 is a graph thatshows such a residual error function. Here spectrometer to spectrometerdiscrepancies can be observed that are on the order of 3 pm peak tovalley, which is well above the desired linearity requirement of anoptical interrogation system 100. This problem is addressed by thepresent invention.

If a low pass filter (without oversampling) was applied to the same setof data, then a residual error function 2102 would be obtained like theone shown in the graph of FIG. 21. As can be seen, the amplitude of theperiodic error is reduced, but a periodic error still remains, with aperiod close to 0.09 nm which corresponds to the size of thespectrometer's pixel. This periodic error remains because of thepixelation of the spectra, and the fact that a 25% threshold level wasused. However, if one oversamples the data by N=5 and then applies a lowpass filter of width 500 pm then they would obtain a residual errorfunction 2104 that is shown in FIG. 21. In this case, the amplitude ofthe residual error function 2104 is dramatically reduced to levels below50 fm. To illustrate this point, FIG. 22 was prepared which shows theresidual error functions of all 8 spectrometers after a oversampling/lowpass filter algorithm was applied on the same scale that was used toprepare the graph in FIG. 20. As can be seen, there is a markedimprovement when the oversampling/low pass filter algorithm of thepresent invention is used. In this example, the noise was reduced to0.03 pm at one standard deviation.

To help illustrate the impact that different threshold levels can haveon the measurement noise when the oversampling/low pass filter algorithmis used, reference is made to FIG. 23. FIG. 23 is a graph thatillustrates how the noise can be reduced when the threshold level isincreased. It is only by using the oversampling based algorithm that onecan increase the threshold level to 10% and above without suffering fromthe penalty of pixelation induced non-linearity. Therefore, such analgorithm is not only important for reducing non-linearities, but it isalso important for allowing one to increase the threshold and it isimportant for obtaining the optimum noise performance from the opticalinterrogation system 100.

FIG. 24 is a flowchart illustrating the steps of a method 2400 for usingthe optical interrogation system 100 to interrogate a biosensor 102 andat the same time reduce the measurement artifacts caused by theproblematical pixelation effect in accordance with the second embodimentof the present invention. Beginning at step 2402, the opticalinterrogation system 100 and in particular a launch system 101 emits anoptical beam 106 towards the biosensor 102. At step 2404, the opticalinterrogation system 100 and in particular a receive system 103 collectsan optical beam 110 from the biosensor 102. At step 2406, the opticalinterrogation system 100 and in particular a spectrometer 114 generatesa signal 116 which corresponds to the collected optical 110. Then atstep 2408, the optical interrogation system 100 and in particular aprocessor 118 reduces pixelation oscillations in the signal 116 by: (a)oversampling at least a portion of the spectral resonance 117; (b)smoothing the spectral resonance 117; and (c) calculating a resonancelocation or centroid that is based on the smoothed-oversampled spectralresonance 117. The resonance location or centroid can be calculated byusing any peak locating routine (e.g., peak fitting to a known resonanceshape, correlation function, or a weighted (or higher order) centroidknife edge). It should be appreciated that the oversampling step and thesmoothing step are interchangeable.

From the foregoing, it should be appreciated by those skilled in the artthat the present invention relates to an algorithm for peak detectionthat can be applied to LID sensors and, more specifically, to a methodfor filtering parasitic fringes and minimizing the detection error thatis generated by the finite pixel size of the detector. This algorithmhas been applied to a broadband spectral interrogation LID instrumentwhere both parasitic fringe filtering and sensor pixel size matter.However, this algorithm could also be applied to other instrumentarchitectures that: (1) interrogate a resonant waveguide gratings with atunable laser; (2) interrogate a resonant waveguide grating sensor withan angular technique; and (3) interrogate a surface plasmon resonancesensor with either a spectral or angular technique.

For a more detailed discussion about the structure of a preferredbiosensor 102 described herein, reference is made to the followingdocuments:

-   -   U.S. Pat. No. 4,815,843 entitled “Optical Sensor for Selective        Detection of Substances and/or for the Detection of Refractive        Index Changes in Gaseous, Liquid, Solid and Porous Samples”.    -   K. Tiefenthaler et al. “Integrated Optical Switches and Gas        Sensors” Opt. Lett. 10, No. 4, April 1984, pp. 137-139.

The contents of these documents are incorporated by reference herein.

Although several embodiments of the present invention have beenillustrated in the accompanying Drawings and described in the foregoingDetailed Description, it should be understood that the invention is notlimited to the embodiments disclosed, but is capable of numerousrearrangements, modifications and substitutions without departing fromthe spirit of the invention as set forth and defined by the followingclaims.

1. An optical interrogation system comprising: a launch system thatemits an optical beam towards a biosensor; a receive system thatcollects an optical beam from the biosensor and then outputs a signalwhich corresponds to the collected optical beam; and a processor thatapplies a low pass filter to the signal to digitally remove a pluralityof problematic parasitic reflections located on each side of an opticalresonance represented within the signal.
 2. The optical interrogationsystem of claim 1, wherein said low pass filter is represented asfollows:Y′=G

y where: y is the signal;

represents a convolution product; G is a filter function; and Y′ is afiltered signal.
 3. The optical interrogation system of claim 2, whereinthe filter function is: a Gaussian function; a rectangular function; ora sinc function.
 4. The optical interrogation system of claim 1, whereinsaid low pass filter is represented as follows:Y1=G*FFT(y); andY′=FFT ⁻¹(Y1) where: y is the signal; G is a filter function; FFT is aFourier transform; FFT⁻¹ is an inverse Fourier transform; and Y′ is afiltered signal.
 5. The optical interrogation system of claim 4, whereinthe filter function is: a Gaussian function; a rectangular function; ora sinc function.
 6. The optical interrogation system of claim 1, furthercomprising an optical isolator located between the biosensor and both alensed fiber of the launch system and a lensed fiber of the receivesystem, wherein the optical isolator filters out the parasiticreflections caused by a part of the optical beam reflected from a faceof a substrate of the biosensor, and wherein the optical isolator doesnot filter out the parasitic reflections caused by the optical beamwhich passes through the substrate and is reflected from a top surfaceof the biosensor.
 7. The optical interrogation system of claim 1,wherein the low pass filter filters out the parasitic reflections causedby the optical beam which passes through a substrate of the biosensorand is then reflected from a top surface of the biosensor, and whereinthe low pass filter does not filter out the parasitic reflections causedby a part of the optical beam reflected from a face of the substrate ofthe biosensor.
 8. The optical interrogation system of claim 1, whereinthe receive system includes a spectrometer which has a misalignedentrance spectrometer slit or fiber.
 9. The optical interrogation systemof claim 1, wherein the low pass filter has a width sized to suppressdetrimental effects of the parasitic reflections.
 10. The opticalinterrogation system of claim 1, wherein the optical resonance includesa spectral resonance with a width that is larger than a period ofparasitic fringes.
 11. The optical interrogation system of claim 1,wherein the receive system samples the optical beam collected from thebiosensor at a period lower than a period of parasitic fringes which areon each side of the optical resonance.
 12. A method for interrogating abiosensor, said method comprising the steps of: emitting an optical beamtowards the biosensor; collecting an optical beam from the biosensor;generating a signal which corresponds to the collected optical beam; andapplying a low pass filter to the signal to digitally remove a pluralityof problematic parasitic reflections located on each side of an opticalresonance represented within the signal.
 13. The method of claim 12,wherein said low pass filter is represented as follows:Y′=G

y where: y is the signal;

represents a convolution product; G is a filter function; and Y′ is afiltered signal.
 14. The method of claim 13, wherein the filter functionis: a Gaussian function; a rectangular function; or a sinc function. 15.The method of claim 12, wherein said low pass filter is represented asfollows:Y1=G*FFT(y); andY′=FFT ⁻¹(Y1) where: y is the signal; G is a filter function; FFT is aFourier transform; FFT⁻¹ is an inverse Fourier transform; and Y′ is afiltered signal.
 16. The method of claim 15, wherein the filter functionis: a Gaussian function; a rectangular function; or a sinc function. 17.The method of claim 12, further comprising a step of using an opticalisolator to filter out the parasitic reflections caused by a part of theoptical beam reflected from a face of a substrate of the biosensor whilethe optical isolator does not filter out the parasitic reflectionscaused by the optical beam which passes through the substrate isreflected from a top surface of the biosensor.
 18. The method of claim12, wherein the low pass filter filters out the parasitic reflectionscaused by the optical beam which passes through a substrate of thebiosensor and is then reflected from a top surface of the biosensor, andwherein the low pass filter does not filter out the parasiticreflections caused by a part of the optical beam reflected from a faceof the substrate of the biosensor.
 19. The method of claim 12, whereinthe optical resonance include a spectral resonance with a width that islarger than a period of parasitic fringes.
 20. The method of claim 12,wherein the collecting step includes a step of sampling the optical beamcollected from the biosensor at a period lower than a period ofparasitic fringes which are on each side of the optical resonance.